Advanced Search
Zhou Benwei, Fan Liping, Zhang Long, Li Poren, Fang Lihua. 2020: Earthquake detection using convolutional neural network and its optimization. Acta Seismologica Sinica, 42(6): 669-683. DOI: 10.11939/jass.20200045
Citation: Zhou Benwei, Fan Liping, Zhang Long, Li Poren, Fang Lihua. 2020: Earthquake detection using convolutional neural network and its optimization. Acta Seismologica Sinica, 42(6): 669-683. DOI: 10.11939/jass.20200045
shu

Earthquake detection using convolutional neural network and its optimization

  • Institute of Geophysics，China Earthquake Administration，Beijing 100081，China

More Information
  • Received Date: March 29, 2019
  • Revised Date: June 28, 2020
  • Available Online: December 30, 2020
  • Published Date: November 14, 2020
    Graphical Abstract
    • Abstract
  • Earthquake detection is the key step of automatic processing such as earthquake quick report and earthquake early warning. In recent years, the use of deep learning algorithm to detect earthquakes has developed rapidly. However, there are few detailed researches on data processing flow and neural network parameter optimization. Taking 8 321 near earthquake data observed by Xichang array as an example, this paper introduces in detail the data processing flow of detecting earthquakes by using the deep convolution neural network, such as data preprocessing, model training, waveform length, network layers, learning rate and probability threshold on the detection results. Then we detect the continuous waveform with the optimal model. Our research shows that data preprocessing, data augmentation can improve the detection accuracy and anti-interference ability of the model. The length of waveform window used for model training can be approximated to the maximum value from arrival time difference between S- and P- wave. The detection results of different network layers （5—8 layers） are similar. For seismic detection, it is more appropriate to set the learning rate as 10−4—10−3. The earthquakes detected by convolution neural network are related to the probability threshold. By drawing the tradeoff curve of precision with recall rate, it can provide a reference for selecting the appropriate probability threshold. This paper provides an important reference for further study of earthquake detection with deep learning algorithm.
    • FullText(HTML)
    • References (38)
  • 蒋策,吴建平,房立华. 2018. 地震检测与震相自动拾取研究[J]. 地震学报,40(1):45–57. doi: 10.11939/jass.20170093
    Jiang C,Wu J P,Fang L H. 2018. Earthquake detection and automatic phase picking[J]. Acta Seismologica Sinica,40(1):45–57 (in Chinese).
    刘芳,蒋一然,宁杰远,张建中,赵艳红. 2020. 结合台阵策略的震相拾取深度学习方法[J]. 科学通报,65(11):1016–1026.
    Liu F,Jiang Y R,Ning J Y,Sun J Z,Zhao Y H. 2020. An array-assisted deep learning approach to seismic phase-picking[J]. Chinese Science Bulletin,65(11):1016–1026 (in Chinese).
    李安. 2019. 基于深度学习的地震事件与震相自动识别[D]. 北京: 中国地震局地球物理研究所: 40–41.
    Li A. 2019. Automatic Recognition of Seismic Events and Phase Picking Based on Deep Learning[D]. Beijing: Institute of Geophysics, China Earthquake Administration: 40–41 （in Chinese）.
    杨旭,李永华,苏伟,孙莲. 2019. 基于优化参数的地震P、S波震相到时自动拾取及质量评估[J]. 地球物理学报,62(11):4290–4299. doi: 10.6038/cjg2019M0059
    Yang X,Li Y H,Su W,Sun L. 2019. Optimized automatic pickers and quality assessment:Application to identification of local and regional P and S wave phases[J]. Chinese Journal of Geophysics,62(11):4290–4299 (in Chinese).
    于子叶,储日升,盛敏汉. 2018. 深度神经网络拾取地震P和S波到时[J]. 地球物理学报,61(12):151–164.
    Yu Z Y,Chu R S,Sheng M H. 2018. Pick onset time of P and S phase by deep neural network[J]. Chinese Journal of Geophysics,61(12):4873–4886 (in Chinese).
    赵明,陈石,房立华,David A Y. 2019. 基于U形卷积神经网络的震相识别与到时拾取方法研究[J]. 地球物理学报,62(8):3034–3042.
    Zhao M,Chen S,Fang L H,David A Y. 2019b. Earthquake phase arrival auto-picking based on U-shaped convolutional neural network[J]. Chinese Journal of Geophysics,62(8):3034–3042 (in Chinese).
    Allen R V. 1978. Automatic earthquake recognition and timing from single traces[J]. Bull Seismol Soc Am,68(5):1521–1532.
    Baer M,Kradolfer U. 1987. An Automatic phase picker for local and teleseismic events[J]. Bull Seismol Soc Am,77(4):1437–1445.
    Baillard C,Crawford W C,Ballu V,Hibert C,Mangeney A. 2014. An automatic kurtosis- based P- and S- phase picker designed for local seismic networks[J]. Bull Seismol Soc Am,104(1):394–409.
    Dokht R M H,Kao H,Visser R,Smith B. 2019. Seismic event and phase detection using time–frequency representation and convolutional neural networks[J]. Seismol Res Lett,90(2A):481–490.
    Earle P S,Shearer P M. 1994. Characterization of global seismograms using an automatic-picking algorithm[J]. Bull Seismol Soc Am,84(2):366–376.
    Gibbons S J,Ringdal F. 2006. The detection of low magnitude seismic events using array-based waveform correlation[J]. Geophys J Int,165(1):149–166.
    Haykin, Simon S. 2010. Neural Networks and Learning Machines[M]. Third Edition. Prentice Hall: Pearson Education India: 146–147.
    Hensman P, Masko D. 2015. The Impact of Imbalanced Training Data for Convolutional Neural Networks[M]. Sweden: KTH Royal Institute of Technology: 1–28.
    Kingma, Diederik P, Jimmy Ba. 2015. Adam: A method for stochastic optimization[EB/OL] [2020–12–17]. https://arxiv.org/abs/1412.6980.
    Kriegerowski M,Petersen G M,Vasyura-Bathke H,Ohrnberger M. 2018. A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms[J]. Seismol Res Lett,90(2A):510–516.
    Krizhevsky A, Sutskever I, Hinton G E. 2012. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems. Tahoe: NV: 1097–1105.
    Mousavi S M,Zhu W Q,Sheng Y X,Beroza G C. 2019. CRED:A deep residual network of convolutional and recurrent units for earthquake signal detection[J]. Scientific Reports,9(1):1–14.
    Nair V, Hinton G E. 2010. Rectified linear units improve restricted boltzmann machines[C]//Proceedings of the 27th International Conf. on Machine Learning ICML-10. Haifa: Israel: 807–814.
    Ng A Y. 2004. Feature selection, L1 vs L2 regularization, and rotational invariance[C]//Proceedings of the Twenty-First International Conference on Machine Learning. Banff: 78.
    Peng Z G,Zhao P. 2009. Migration of early aftershocks following the 2004 Parkfield earthquake[J]. Nat Geosci,2(12):877–881.
    Powers,David M W. 2011. Evaluation:From precision,recall and F-measure to roc,informedness,markedness and correlation[J]. J Mach Learning Techn,2(1):37–63.
    Perol T,Gharbi M,Denolle M. 2018. Convolutional neural network for earthquake detection and location[J]. Sci Adv,4(2):e1700578.
    Ross Z E,Meier M A,Hauksson E,Heaton T H. 2018a. Generalized seismic phase detection with deep learning[J]. Bull Seismol Soc Am,108(5A):2894–2901.
    Ross Z E,Meier M A,Hauksson E. 2018b. P wave arrival picking and first-motion polarity determination with deep learning[J]. J Geophys Res,123(6):5120–5129.
    Saragiotis C D,Hadjileontiadis L J,Panas S M. 2002. PAI-S/K:A robust automatic seismic P phase arrival identification scheme[J]. IEEE T Geosci Remote,40(6):1395–1404.
    Sleeman R,van Eck T V. 1999. Robust automatic P-phase picking:An on-line implementation in the analysis of broadband seismogram recordings[J]. Phys Earth Planet In,113(1-4):0–275.
    Wang J,Xiao Z W,Liu C,Zhao D P,Yao Z X. 2019. Deep learning for picking seismic arrival times[J]. J Geophys Res,124(7):6612–6624.
    Wilson D R,Martinez T R. 2003. The general inefficiency of batch training for gradient descent learning[J]. Neural Netw,16:1429–1451.
    Yoon C E,O’Reilly O,Bergen K J. 2015. Earthquake detection through computationally efficient similarity search[J]. Sci Adv,1(11):e1501057.
    Zhou Y J,Yue H,Kong Q K,Zhou S Y. 2019. Hybrid event detection and phase-picking algorithm using convolutional andrecurrent neural networks[J]. Seismol Res Lett,90(3):1079–1087.
    Zhu W Q,Beroza G C. 2018. PhaseNet:A deep-neural-network-based seismic arrival-time picking method[J]. Geophys J Int,216(1):261–273.
    • Related Articles

Catalog

    Get Citation
    PDF
    XML
    Article views (1529) PDF downloads (185) Cited by()
    Turn off MathJax
    Article Contents
    Abstract
    References

    Supported by: Beijing Renhe Information Technology Co., Ltd.   

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return